{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Isotonic Regression and Linear Regression"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. (Wikipedia)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.collections import LineCollection\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yfinance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:13.969Z",
          "iopub.execute_input": "2021-02-09T01:44:13.988Z",
          "iopub.status.idle": "2021-02-09T01:44:16.013Z",
          "shell.execute_reply": "2021-02-09T01:44:16.109Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-02-09T01:44:17.897Z",
          "iopub.status.busy": "2021-02-09T01:44:16.057Z",
          "iopub.execute_input": "2021-02-09T01:44:16.076Z",
          "iopub.status.idle": "2021-02-09T01:44:17.365Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:17.417Z",
          "iopub.execute_input": "2021-02-09T01:44:17.506Z",
          "iopub.status.idle": "2021-02-09T01:44:17.599Z",
          "shell.execute_reply": "2021-02-09T01:44:17.909Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Increase_Decrease  \\\nDate                                                                          \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n\n            Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                              \n2014-01-03                 1         1  0.012658  \n2014-01-06                 1         1  0.032500  \n2014-01-07                 1         0  0.012106  \n2014-01-08                 0         0  0.000000  \n2014-01-09                 0         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:17.672Z",
          "iopub.execute_input": "2021-02-09T01:44:17.694Z",
          "iopub.status.idle": "2021-02-09T01:44:17.732Z",
          "shell.execute_reply": "2021-02-09T01:44:17.918Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n = len(dataset['Adj Close'])\n",
        "X = np.array(dataset['Open'].values)\n",
        "y = dataset['Adj Close'].values"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:17.754Z",
          "iopub.execute_input": "2021-02-09T01:44:17.768Z",
          "iopub.status.idle": "2021-02-09T01:44:17.792Z",
          "shell.execute_reply": "2021-02-09T01:44:17.929Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.isotonic import IsotonicRegression"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:17.816Z",
          "iopub.execute_input": "2021-02-09T01:44:17.834Z",
          "shell.execute_reply": "2021-02-09T01:44:21.308Z",
          "iopub.status.idle": "2021-02-09T01:44:21.338Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ir=IsotonicRegression()\n",
        "lr=LinearRegression()\n",
        "y_ir=ir.fit_transform(X,y)\n",
        "lr.fit(X[:,np.newaxis],y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:21.365Z",
          "iopub.execute_input": "2021-02-09T01:44:21.380Z",
          "iopub.status.idle": "2021-02-09T01:44:21.417Z",
          "shell.execute_reply": "2021-02-09T01:44:21.632Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lines=[[[i,y[i]],[i,y_ir[i]]] for i in range(n)]\n",
        "lc=LineCollection(lines)\n",
        "plt.figure(figsize=(16,4))\n",
        "plt.plot(X,y,'r.',markersize=12)\n",
        "plt.plot(X,y_ir,'b.-',markersize=12)\n",
        "plt.plot(X,lr.predict(X[:, np.newaxis]), 'y-')\n",
        "plt.gca().add_collection(lc)\n",
        "plt.legend(('Data','Isotonic Fit','Linear Fit'))\n",
        "plt.title(\"Isotonic Regression and Linear Regression\")"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Isotonic Regression')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1152x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-02-09T01:44:21.462Z",
          "iopub.execute_input": "2021-02-09T01:44:21.518Z",
          "iopub.status.idle": "2021-02-09T01:44:22.176Z",
          "shell.execute_reply": "2021-02-09T01:44:22.206Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}